import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook
%matplotlib inline

import tensorflow
print (tensorflow.__version__)

2.4.0

!wget https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/apple2orange.zip

--2020-12-27 08:22:37--  https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/apple2orange.zip
Resolving people.eecs.berkeley.edu (people.eecs.berkeley.edu)... 128.32.244.190
Connecting to people.eecs.berkeley.edu (people.eecs.berkeley.edu)|128.32.244.190|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 78456409 (75M) [application/zip]
Saving to: ‘apple2orange.zip’

apple2orange.zip    100%[===================>]  74.82M  3.48MB/s    in 25s     

2020-12-27 08:23:02 (3.03 MB/s) - ‘apple2orange.zip’ saved [78456409/78456409]

!unzip apple2orange.zip

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!ls apple2orange

testA  testB  trainA  trainB

import glob
apples_train = glob.glob('apple2orange/trainA/*.jpg')
oranges_train = glob.glob('apple2orange/trainB/*.jpg')
apples_test = glob.glob('apple2orange/testA/*.jpg')
oranges_test = glob.glob('apple2orange/testB/*.jpg')

len(apples_train), len(oranges_train), len(apples_test), len(oranges_test)

(995, 1019, 266, 248)

import cv2
for file in apples_train[:10]:
    img = cv2.imread(file)
    print (img.shape)

(256, 256, 3)
(256, 256, 3)
(256, 256, 3)
(256, 256, 3)
(256, 256, 3)
(256, 256, 3)
(256, 256, 3)
(256, 256, 3)
(256, 256, 3)
(256, 256, 3)

print ("Apples")
for k in range(5):
    plt.figure(figsize=(13, 13))
    for j in range(9):
        file = np.random.choice(apples_train)
        img = cv2.imread(file)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        plt.subplot(990 + 1 + j)
        plt.imshow(img)
        plt.axis('off')
        #plt.title(trainY[i])
    plt.show()

print ("-"*80)
print ("Oranges")
for k in range(5):
    plt.figure(figsize=(13, 13))
    for j in range(9):
        file = np.random.choice(oranges_train)
        img = cv2.imread(file)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        plt.subplot(990 + 1 + j)
        plt.imshow(img)
        plt.axis('off')
        #plt.title(trainY[i])
    plt.show()

Apples

--------------------------------------------------------------------------------
Oranges

Generator Model (U-Net Like)

import tensorflow_addons as tfa

def encoder_layer(input_layer, filters, bn=True):
    x = tensorflow.keras.layers.Conv2D(filters, kernel_size=(4,4), strides=(2,2), padding='same')(input_layer)
    x = tensorflow.keras.layers.LeakyReLU(alpha=0.2)(x)
    if bn:
        #x = tensorflow.keras.layers.BatchNormalization(momentum=0.8)(x)
        x = tfa.layers.InstanceNormalization()(x)
    return x

def decoder_layer(input_layer, skip_input, filters):
    #x = tensorflow.keras.layers.UpSampling2D(size=2)(input_layer)
    x = tensorflow.keras.layers.Conv2DTranspose(filters, kernel_size=(4,4), strides=(2,2), padding='same')(input_layer)
    x = tensorflow.keras.layers.Activation('relu')(x)
    #x = tensorflow.keras.layers.BatchNormalization(momentum=0.8)(x)
    x = tfa.layers.InstanceNormalization()(x)
    x = tensorflow.keras.layers.Concatenate()([x, skip_input])
    return x

source_image_A = tensorflow.keras.layers.Input(shape=(256, 256, 3))
source_image_B = tensorflow.keras.layers.Input(shape=(256, 256, 3))

def make_generator():
    source_image = tensorflow.keras.layers.Input(shape=(256, 256, 3))

    e1 = encoder_layer(source_image, 32, bn=False)
    e2 = encoder_layer(e1, 64)
    e3 = encoder_layer(e2, 128)
    e4 = encoder_layer(e3, 256)
    e5 = encoder_layer(e4, 256)
    e6 = encoder_layer(e5, 256)
    e7 = encoder_layer(e6, 256)

    bottle_neck = tensorflow.keras.layers.Conv2D(512, (4,4), strides=(2,2), padding='same')(e7)
    b = tensorflow.keras.layers.Activation('relu')(bottle_neck)

    d1 = decoder_layer(b, e7, 256)
    d2 = decoder_layer(d1, e6, 256)
    d3 = decoder_layer(d2, e5, 256)
    d4 = decoder_layer(d3, e4, 256)
    d5 = decoder_layer(d4, e3, 256)
    d6 = decoder_layer(d5, e2, 64)
    d7 = decoder_layer(d6, e1, 32)

    decoded = tensorflow.keras.layers.Conv2DTranspose(3, kernel_size=(4,4), strides=(2,2), padding='same')(d7)
    translated_image = tensorflow.keras.layers.Activation('tanh')(decoded)
    return source_image, translated_image

source_image, translated_image = make_generator()
generator_network_AB = tensorflow.keras.models.Model(inputs=source_image, outputs=translated_image)

source_image, translated_image = make_generator()
generator_network_BA = tensorflow.keras.models.Model(inputs=source_image, outputs=translated_image)

print (generator_network_AB.summary())

Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_3 (InputLayer)            [(None, 256, 256, 3) 0                                            
__________________________________________________________________________________________________
conv2d (Conv2D)                 (None, 128, 128, 32) 1568        input_3[0][0]                    
__________________________________________________________________________________________________
leaky_re_lu (LeakyReLU)         (None, 128, 128, 32) 0           conv2d[0][0]                     
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 64, 64, 64)   32832       leaky_re_lu[0][0]                
__________________________________________________________________________________________________
leaky_re_lu_1 (LeakyReLU)       (None, 64, 64, 64)   0           conv2d_1[0][0]                   
__________________________________________________________________________________________________
instance_normalization (Instanc (None, 64, 64, 64)   128         leaky_re_lu_1[0][0]              
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 32, 32, 128)  131200      instance_normalization[0][0]     
__________________________________________________________________________________________________
leaky_re_lu_2 (LeakyReLU)       (None, 32, 32, 128)  0           conv2d_2[0][0]                   
__________________________________________________________________________________________________
instance_normalization_1 (Insta (None, 32, 32, 128)  256         leaky_re_lu_2[0][0]              
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 16, 16, 256)  524544      instance_normalization_1[0][0]   
__________________________________________________________________________________________________
leaky_re_lu_3 (LeakyReLU)       (None, 16, 16, 256)  0           conv2d_3[0][0]                   
__________________________________________________________________________________________________
instance_normalization_2 (Insta (None, 16, 16, 256)  512         leaky_re_lu_3[0][0]              
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 8, 8, 256)    1048832     instance_normalization_2[0][0]   
__________________________________________________________________________________________________
leaky_re_lu_4 (LeakyReLU)       (None, 8, 8, 256)    0           conv2d_4[0][0]                   
__________________________________________________________________________________________________
instance_normalization_3 (Insta (None, 8, 8, 256)    512         leaky_re_lu_4[0][0]              
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 4, 4, 256)    1048832     instance_normalization_3[0][0]   
__________________________________________________________________________________________________
leaky_re_lu_5 (LeakyReLU)       (None, 4, 4, 256)    0           conv2d_5[0][0]                   
__________________________________________________________________________________________________
instance_normalization_4 (Insta (None, 4, 4, 256)    512         leaky_re_lu_5[0][0]              
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, 2, 2, 256)    1048832     instance_normalization_4[0][0]   
__________________________________________________________________________________________________
leaky_re_lu_6 (LeakyReLU)       (None, 2, 2, 256)    0           conv2d_6[0][0]                   
__________________________________________________________________________________________________
instance_normalization_5 (Insta (None, 2, 2, 256)    512         leaky_re_lu_6[0][0]              
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, 1, 1, 512)    2097664     instance_normalization_5[0][0]   
__________________________________________________________________________________________________
activation (Activation)         (None, 1, 1, 512)    0           conv2d_7[0][0]                   
__________________________________________________________________________________________________
conv2d_transpose (Conv2DTranspo (None, 2, 2, 256)    2097408     activation[0][0]                 
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 2, 2, 256)    0           conv2d_transpose[0][0]           
__________________________________________________________________________________________________
instance_normalization_6 (Insta (None, 2, 2, 256)    512         activation_1[0][0]               
__________________________________________________________________________________________________
concatenate (Concatenate)       (None, 2, 2, 512)    0           instance_normalization_6[0][0]   
                                                                 instance_normalization_5[0][0]   
__________________________________________________________________________________________________
conv2d_transpose_1 (Conv2DTrans (None, 4, 4, 256)    2097408     concatenate[0][0]                
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 4, 4, 256)    0           conv2d_transpose_1[0][0]         
__________________________________________________________________________________________________
instance_normalization_7 (Insta (None, 4, 4, 256)    512         activation_2[0][0]               
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 4, 4, 512)    0           instance_normalization_7[0][0]   
                                                                 instance_normalization_4[0][0]   
__________________________________________________________________________________________________
conv2d_transpose_2 (Conv2DTrans (None, 8, 8, 256)    2097408     concatenate_1[0][0]              
__________________________________________________________________________________________________
activation_3 (Activation)       (None, 8, 8, 256)    0           conv2d_transpose_2[0][0]         
__________________________________________________________________________________________________
instance_normalization_8 (Insta (None, 8, 8, 256)    512         activation_3[0][0]               
__________________________________________________________________________________________________
concatenate_2 (Concatenate)     (None, 8, 8, 512)    0           instance_normalization_8[0][0]   
                                                                 instance_normalization_3[0][0]   
__________________________________________________________________________________________________
conv2d_transpose_3 (Conv2DTrans (None, 16, 16, 256)  2097408     concatenate_2[0][0]              
__________________________________________________________________________________________________
activation_4 (Activation)       (None, 16, 16, 256)  0           conv2d_transpose_3[0][0]         
__________________________________________________________________________________________________
instance_normalization_9 (Insta (None, 16, 16, 256)  512         activation_4[0][0]               
__________________________________________________________________________________________________
concatenate_3 (Concatenate)     (None, 16, 16, 512)  0           instance_normalization_9[0][0]   
                                                                 instance_normalization_2[0][0]   
__________________________________________________________________________________________________
conv2d_transpose_4 (Conv2DTrans (None, 32, 32, 256)  2097408     concatenate_3[0][0]              
__________________________________________________________________________________________________
activation_5 (Activation)       (None, 32, 32, 256)  0           conv2d_transpose_4[0][0]         
__________________________________________________________________________________________________
instance_normalization_10 (Inst (None, 32, 32, 256)  512         activation_5[0][0]               
__________________________________________________________________________________________________
concatenate_4 (Concatenate)     (None, 32, 32, 384)  0           instance_normalization_10[0][0]  
                                                                 instance_normalization_1[0][0]   
__________________________________________________________________________________________________
conv2d_transpose_5 (Conv2DTrans (None, 64, 64, 64)   393280      concatenate_4[0][0]              
__________________________________________________________________________________________________
activation_6 (Activation)       (None, 64, 64, 64)   0           conv2d_transpose_5[0][0]         
__________________________________________________________________________________________________
instance_normalization_11 (Inst (None, 64, 64, 64)   128         activation_6[0][0]               
__________________________________________________________________________________________________
concatenate_5 (Concatenate)     (None, 64, 64, 128)  0           instance_normalization_11[0][0]  
                                                                 instance_normalization[0][0]     
__________________________________________________________________________________________________
conv2d_transpose_6 (Conv2DTrans (None, 128, 128, 32) 65568       concatenate_5[0][0]              
__________________________________________________________________________________________________
activation_7 (Activation)       (None, 128, 128, 32) 0           conv2d_transpose_6[0][0]         
__________________________________________________________________________________________________
instance_normalization_12 (Inst (None, 128, 128, 32) 64          activation_7[0][0]               
__________________________________________________________________________________________________
concatenate_6 (Concatenate)     (None, 128, 128, 64) 0           instance_normalization_12[0][0]  
                                                                 leaky_re_lu[0][0]                
__________________________________________________________________________________________________
conv2d_transpose_7 (Conv2DTrans (None, 256, 256, 3)  3075        concatenate_6[0][0]              
__________________________________________________________________________________________________
activation_8 (Activation)       (None, 256, 256, 3)  0           conv2d_transpose_7[0][0]         
==================================================================================================
Total params: 16,888,451
Trainable params: 16,888,451
Non-trainable params: 0
__________________________________________________________________________________________________
None

Discriminator Network

def my_conv_layer(input_layer, filters, bn=True):
    x = tensorflow.keras.layers.Conv2D(filters, kernel_size=(4,4), strides=(2,2), padding='same')(input_layer)
    x = tensorflow.keras.layers.LeakyReLU(alpha=0.2)(x)
    if bn:
        #x = tensorflow.keras.layers.BatchNormalization(momentum=0.8)(x)
        x = tfa.layers.InstanceNormalization()(x)
    return x

 def make_discriminator():
    target_image_input = tensorflow.keras.layers.Input(shape=(256, 256, 3))

    x = my_conv_layer(target_image_input, 64, bn=False)
    x = my_conv_layer(x, 128)
    x = my_conv_layer(x, 256)
    x = my_conv_layer(x, 512)
    x = my_conv_layer(x, 512)

    patch_features = tensorflow.keras.layers.Conv2D(1, kernel_size=(4,4), strides=(1,1), padding='same')(x)
    return target_image_input, patch_features


target_image_input, patch_features = make_discriminator()
discriminator_network_A = tensorflow.keras.models.Model(inputs=target_image_input, outputs=patch_features)

target_image_input, patch_features = make_discriminator()
discriminator_network_B = tensorflow.keras.models.Model(inputs=target_image_input, outputs=patch_features)

print (discriminator_network_A.summary())

Model: "model_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_5 (InputLayer)         [(None, 256, 256, 3)]     0         
_________________________________________________________________
conv2d_16 (Conv2D)           (None, 128, 128, 64)      3136      
_________________________________________________________________
leaky_re_lu_14 (LeakyReLU)   (None, 128, 128, 64)      0         
_________________________________________________________________
conv2d_17 (Conv2D)           (None, 64, 64, 128)       131200    
_________________________________________________________________
leaky_re_lu_15 (LeakyReLU)   (None, 64, 64, 128)       0         
_________________________________________________________________
instance_normalization_26 (I (None, 64, 64, 128)       256       
_________________________________________________________________
conv2d_18 (Conv2D)           (None, 32, 32, 256)       524544    
_________________________________________________________________
leaky_re_lu_16 (LeakyReLU)   (None, 32, 32, 256)       0         
_________________________________________________________________
instance_normalization_27 (I (None, 32, 32, 256)       512       
_________________________________________________________________
conv2d_19 (Conv2D)           (None, 16, 16, 512)       2097664   
_________________________________________________________________
leaky_re_lu_17 (LeakyReLU)   (None, 16, 16, 512)       0         
_________________________________________________________________
instance_normalization_28 (I (None, 16, 16, 512)       1024      
_________________________________________________________________
conv2d_20 (Conv2D)           (None, 8, 8, 512)         4194816   
_________________________________________________________________
leaky_re_lu_18 (LeakyReLU)   (None, 8, 8, 512)         0         
_________________________________________________________________
instance_normalization_29 (I (None, 8, 8, 512)         1024      
_________________________________________________________________
conv2d_21 (Conv2D)           (None, 8, 8, 1)           8193      
=================================================================
Total params: 6,962,369
Trainable params: 6,962,369
Non-trainable params: 0
_________________________________________________________________
None

adam_optimizer = tensorflow.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)
discriminator_network_A.compile(loss='mse', optimizer=adam_optimizer, metrics=['accuracy'])
discriminator_network_B.compile(loss='mse', optimizer=adam_optimizer, metrics=['accuracy'])

Cycle-GAN

# Domain Transfer
fake_B = generator_network_AB(source_image_A)
fake_A = generator_network_BA(source_image_B)

# Restoring original Domain
get_back_A = generator_network_BA(fake_B)
get_back_B = generator_network_AB(fake_A)

# Get back Identical/Same Image
get_same_A = generator_network_BA(source_image_A)
get_same_B = generator_network_AB(source_image_B)
  
discriminator_network_A.trainable=False
discriminator_network_B.trainable=False

# Tell Real vs Fake, for a given domain
verify_A = discriminator_network_A(fake_A)
verify_B = discriminator_network_B(fake_B)

cycle_gan = tensorflow.keras.models.Model(inputs = [source_image_A, source_image_B], \
                              outputs = [verify_A, verify_B, get_back_A, get_back_B, get_same_A, get_same_B])
cycle_gan.summary()

Model: "model_4"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_2 (InputLayer)            [(None, 256, 256, 3) 0                                            
__________________________________________________________________________________________________
input_1 (InputLayer)            [(None, 256, 256, 3) 0                                            
__________________________________________________________________________________________________
model_1 (Functional)            (None, 256, 256, 3)  16888451    input_2[0][0]                    
                                                                 model[0][0]                      
                                                                 input_1[0][0]                    
__________________________________________________________________________________________________
model (Functional)              (None, 256, 256, 3)  16888451    input_1[0][0]                    
                                                                 model_1[0][0]                    
                                                                 input_2[0][0]                    
__________________________________________________________________________________________________
model_2 (Functional)            (None, 8, 8, 1)      6962369     model_1[0][0]                    
__________________________________________________________________________________________________
model_3 (Functional)            (None, 8, 8, 1)      6962369     model[0][0]                      
==================================================================================================
Total params: 47,701,640
Trainable params: 33,776,902
Non-trainable params: 13,924,738
__________________________________________________________________________________________________

Compiling Models

cycle_gan.compile(loss=['mse', 'mse', 'mae', 'mae', 'mae', 'mae'], loss_weights=[1, 1, 10, 10, 3, 3],\
                                          optimizer=adam_optimizer)

Define Data Generators

def apples_to_oranges(apples, generator_network):
    generated_samples = generator_network.predict_on_batch(apples)
    return generated_samples

def oranges_to_apples(oranges, generator_network):
    generated_samples = generator_network.predict_on_batch(oranges)
    return generated_samples

def get_apple_samples(batch_size):
    random_files = np.random.choice(apples_train, size=batch_size)
    images = []
    for file in random_files:
        img = cv2.imread(file)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        images.append((img-127.5)/127.5)
    apple_images = np.array(images)
    return apple_images

def get_orange_samples(batch_size):
    random_files = np.random.choice(oranges_train, size=batch_size)
    images = []
    for file in random_files:
        img = cv2.imread(file)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        images.append((img-127.5)/127.5)
    orange_images = np.array(images)
    return orange_images

def show_generator_results_apples_to_oranges(generator_network_AB, generator_network_BA):
    images = []
    for j in range(7):
        file = np.random.choice(apples_test)
        img = cv2.imread(file)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        images.append(img)

    print ('Input Apple Images')
    plt.figure(figsize=(13, 13))
    for j, img in enumerate(images):
        plt.subplot(770 + 1 + j)
        plt.imshow(img)
        plt.axis('off')
        #plt.title(trainY[i])
    plt.show()

    print ('Translated (Apple -> Orange) Images')
    translated = []
    plt.figure(figsize=(13, 13))
    for j, img in enumerate(images):
        img = (img-127.5)/127.5
        output = apples_to_oranges(np.array([img]), generator_network_AB)[0]
        translated.append(output)
        output = (output+1.0)/2.0
        plt.subplot(770 + 1 + j)
        plt.imshow(output)
        plt.axis('off')
        #plt.title(trainY[i])
    plt.show()

    print ('Translated reverse ( Fake Oranges -> Fake Apples)')
    plt.figure(figsize=(13, 13))
    for j, img in enumerate(translated):
        output = oranges_to_apples(np.array([img]), generator_network_BA)[0]
        output = (output+1.0)/2.0
        plt.subplot(770 + 1 + j)
        plt.imshow(output)
        plt.axis('off')
        #plt.title(trainY[i])
    plt.show()

def show_generator_results_oranges_to_apples(generator_network_AB, generator_network_BA):
    images = []
    for j in range(7):
        file = np.random.choice(oranges_test)
        img = cv2.imread(file)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        images.append(img)

    print ('Input Orange Images')
    plt.figure(figsize=(13, 13))
    for j, img in enumerate(images):
        plt.subplot(770 + 1 + j)
        plt.imshow(img)
        plt.axis('off')
        #plt.title(trainY[i])
    plt.show()

    print ('Translated (Orange -> Apple) Images')
    translated = []
    plt.figure(figsize=(13, 13))
    for j, img in enumerate(images):
        img = (img-127.5)/127.5
        output = oranges_to_apples(np.array([img]), generator_network_BA)[0]
        translated.append(output)
        output = (output+1.0)/2.0
        plt.subplot(770 + 1 + j)
        plt.imshow(output)
        plt.axis('off')
        #plt.title(trainY[i])
    plt.show()

    print ('Translated reverse (Fake Apples -> Fake Oranges)')
    plt.figure(figsize=(13, 13))
    for j, img in enumerate(translated):
        output = apples_to_oranges(np.array([img]), generator_network_AB)[0]
        output = (output+1.0)/2.0
        plt.subplot(770 + 1 + j)
        plt.imshow(output)
        plt.axis('off')
        #plt.title(trainY[i])
    plt.show()

Training GAN

len(apples_train), len(oranges_train)

(995, 1019)

epochs = 500
batch_size = 1
steps = 1000

for i in range(0, epochs):
    for j in range(steps):  
        if j%100 == 0:
            show_generator_results_apples_to_oranges(generator_network_AB, generator_network_BA)
            print ("-"*100)
            show_generator_results_oranges_to_apples(generator_network_AB, generator_network_BA)

        # A == Apples
        # B == Oranges
        domain_A_images = get_apple_samples(batch_size)
        domain_B_images = get_orange_samples(batch_size)

        fake_patch = np.zeros((batch_size, 8, 8, 1))
        real_patch = np.ones((batch_size, 8, 8, 1))
        
        fake_B_images = generator_network_AB(domain_A_images)
        fake_A_images = generator_network_BA(domain_B_images)
        
        # Updating Discriminator A weights
        discriminator_network_A.trainable=True
        discriminator_network_B.trainable=False
        loss_d_real_A = discriminator_network_A.train_on_batch(domain_A_images, real_patch)
        loss_d_fake_A = discriminator_network_A.train_on_batch(fake_A_images, fake_patch)
        
        loss_d_A = np.add(loss_d_real_A, loss_d_fake_A)/2.0
        
        # Updating Discriminator B weights
        discriminator_network_B.trainable=True
        discriminator_network_A.trainable=False
        loss_d_real_B = discriminator_network_B.train_on_batch(domain_B_images, real_patch)
        loss_d_fake_B = discriminator_network_B.train_on_batch(fake_B_images, fake_patch)
        
        loss_d_B = np.add(loss_d_real_B, loss_d_fake_B)/2.0
        
        # Make the Discriminator belive that these are real samples and calculate loss to train the generator
        
        discriminator_network_A.trainable=False
        discriminator_network_B.trainable=False
        
        # Updating Generator weights
        loss_g = cycle_gan.train_on_batch([domain_A_images, domain_B_images],\
                    [real_patch, real_patch, domain_A_images, domain_B_images, domain_A_images, domain_B_images])
        
        if j%50 == 0:
            print ("Epoch:%.0f, Step:%.0f, DA-Loss:%.3f, DA-Acc:%.3f, DB-Loss:%.3f, DB-Acc:%.3f, G-Loss:%.3f"\
                   %(i,j,loss_d_A[0],loss_d_A[1]*100,loss_d_B[0],loss_d_B[1]*100,loss_g[0]))
            

Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:0, Step:0, DA-Loss:2.378, DA-Acc:53.906, DB-Loss:1.842, DB-Acc:48.438, G-Loss:131.370
Epoch:0, Step:50, DA-Loss:0.343, DA-Acc:53.906, DB-Loss:0.479, DB-Acc:44.531, G-Loss:11.231
Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:0, Step:100, DA-Loss:0.317, DA-Acc:62.500, DB-Loss:0.332, DB-Acc:64.062, G-Loss:15.323
Epoch:0, Step:150, DA-Loss:0.325, DA-Acc:51.562, DB-Loss:0.407, DB-Acc:66.406, G-Loss:9.195
Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:0, Step:200, DA-Loss:0.305, DA-Acc:53.125, DB-Loss:0.192, DB-Acc:87.500, G-Loss:10.828
Epoch:0, Step:250, DA-Loss:0.180, DA-Acc:75.000, DB-Loss:0.208, DB-Acc:75.000, G-Loss:10.055
Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:0, Step:300, DA-Loss:0.287, DA-Acc:56.250, DB-Loss:0.113, DB-Acc:83.594, G-Loss:7.022
Epoch:0, Step:350, DA-Loss:0.232, DA-Acc:64.844, DB-Loss:0.283, DB-Acc:64.844, G-Loss:5.660
Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:0, Step:400, DA-Loss:0.266, DA-Acc:62.500, DB-Loss:0.366, DB-Acc:68.750, G-Loss:5.058
Epoch:0, Step:450, DA-Loss:0.311, DA-Acc:54.688, DB-Loss:0.273, DB-Acc:53.906, G-Loss:4.695
Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:0, Step:500, DA-Loss:0.339, DA-Acc:63.281, DB-Loss:0.196, DB-Acc:74.219, G-Loss:5.595
Epoch:0, Step:550, DA-Loss:0.300, DA-Acc:53.906, DB-Loss:0.263, DB-Acc:55.469, G-Loss:4.282
Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:0, Step:600, DA-Loss:0.228, DA-Acc:71.094, DB-Loss:0.155, DB-Acc:81.250, G-Loss:5.275
Epoch:0, Step:650, DA-Loss:0.232, DA-Acc:66.406, DB-Loss:0.229, DB-Acc:70.312, G-Loss:5.078
Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:0, Step:700, DA-Loss:0.380, DA-Acc:50.000, DB-Loss:0.280, DB-Acc:60.156, G-Loss:4.706
Epoch:0, Step:750, DA-Loss:0.412, DA-Acc:48.438, DB-Loss:0.333, DB-Acc:42.969, G-Loss:4.530
Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:0, Step:800, DA-Loss:0.277, DA-Acc:63.281, DB-Loss:0.190, DB-Acc:73.438, G-Loss:5.279
Epoch:0, Step:850, DA-Loss:0.477, DA-Acc:50.000, DB-Loss:0.514, DB-Acc:35.156, G-Loss:4.291
Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:0, Step:900, DA-Loss:0.294, DA-Acc:64.844, DB-Loss:0.257, DB-Acc:64.062, G-Loss:3.601
Epoch:0, Step:950, DA-Loss:0.184, DA-Acc:70.312, DB-Loss:0.199, DB-Acc:65.625, G-Loss:3.994
Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:1, Step:0, DA-Loss:0.218, DA-Acc:64.062, DB-Loss:0.302, DB-Acc:49.219, G-Loss:4.235
Epoch:1, Step:50, DA-Loss:0.224, DA-Acc:63.281, DB-Loss:0.256, DB-Acc:59.375, G-Loss:4.318
Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:1, Step:100, DA-Loss:0.449, DA-Acc:26.562, DB-Loss:0.363, DB-Acc:40.625, G-Loss:3.745
Epoch:1, Step:150, DA-Loss:0.163, DA-Acc:80.469, DB-Loss:0.205, DB-Acc:69.531, G-Loss:4.706
Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:1, Step:200, DA-Loss:0.124, DA-Acc:89.844, DB-Loss:0.133, DB-Acc:81.250, G-Loss:4.491
Epoch:1, Step:250, DA-Loss:0.184, DA-Acc:75.000, DB-Loss:0.177, DB-Acc:72.656, G-Loss:4.338
Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:1, Step:300, DA-Loss:0.250, DA-Acc:56.250, DB-Loss:0.597, DB-Acc:48.438, G-Loss:3.750
Epoch:1, Step:350, DA-Loss:0.175, DA-Acc:79.688, DB-Loss:0.204, DB-Acc:73.438, G-Loss:4.371
Input Apple Images

Translated (Apple -> Orange) Images

Translated reverse ( Fake Oranges -> Fake Apples)

----------------------------------------------------------------------------------------------------
Input Orange Images

Translated (Orange -> Apple) Images

Translated reverse (Fake Apples -> Fake Oranges)

Epoch:1, Step:400, DA-Loss:0.451, DA-Acc:45.312, DB-Loss:0.381, DB-Acc:37.500, G-Loss:4.541

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-18-802935a84be5> in <module>()
     43 
     44         # Updating Generator weights
---> 45         loss_g = cycle_gan.train_on_batch([domain_A_images, domain_B_images],                    [real_patch, real_patch, domain_A_images, domain_B_images, domain_A_images, domain_B_images])
     46 
     47         if j%50 == 0:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in train_on_batch(self, x, y, sample_weight, class_weight, reset_metrics, return_dict)
   1725                                                     class_weight)
   1726       self.train_function = self.make_train_function()
-> 1727       logs = self.train_function(iterator)
   1728 
   1729     if reset_metrics:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    826     tracing_count = self.experimental_get_tracing_count()
    827     with trace.Trace(self._name) as tm:
--> 828       result = self._call(*args, **kwds)
    829       compiler = "xla" if self._experimental_compile else "nonXla"
    830       new_tracing_count = self.experimental_get_tracing_count()

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    853       # In this case we have created variables on the first call, so we run the
    854       # defunned version which is guaranteed to never create variables.
--> 855       return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
    856     elif self._stateful_fn is not None:
    857       # Release the lock early so that multiple threads can perform the call

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
   2941        filtered_flat_args) = self._maybe_define_function(args, kwargs)
   2942     return graph_function._call_flat(
-> 2943         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access
   2944 
   2945   @property

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1917       # No tape is watching; skip to running the function.
   1918       return self._build_call_outputs(self._inference_function.call(
-> 1919           ctx, args, cancellation_manager=cancellation_manager))
   1920     forward_backward = self._select_forward_and_backward_functions(
   1921         args,

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
    558               inputs=args,
    559               attrs=attrs,
--> 560               ctx=ctx)
    561         else:
    562           outputs = execute.execute_with_cancellation(

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     58     ctx.ensure_initialized()
     59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60                                         inputs, attrs, num_outputs)
     61   except core._NotOkStatusException as e:
     62     if name is not None:

KeyboardInterrupt: